1. Field of Disclosure
This disclosure relates generally to the fields of computer-aided diagnosis, quantitative image analysis, and image display workstations. Such systems can output a feature value (e.g., characteristic; image-based phenotype) or an estimate of a lesion's probability of disease state (PM) (which can be a probability of malignancy, cancer subtypes, risk, prognostic state, and/or response to treatment), usually determined by training a classifier on datasets.
2. Discussion of the Background
Breast cancer is a leading cause of death in women, causing an estimated 46,000 deaths per year. Mammography is an effective method for the early detection of breast cancer, and it has been shown that periodic screening of asymptomatic women does reduce mortality. Many breast cancers are detected and referred for surgical biopsy on the basis of a radiographically detected mass lesion or a cluster of microcalcifications. Although general rules for the differentiation between benign and malignant mammographically identified breast lesions exist, considerable misclassification of lesions occurs with current methods. On average, less than 30% of masses referred for surgical breast biopsy are actually malignant.
The clinical management and outcome of women with breast cancer vary. Various prognostic indicators can be used in management including patient age, tumor size, number of involved lymph nodes, sites of recurrence, disease free interval, estrogen receptor expression, as well as newer biological markers. It has been shown that in many cases biologic features of the primary tumor can be correlated with outcome, although methods of assessing the biologic features may be invasive, expensive or not widely available. Macroscopic lesion analysis via medical imaging has been quite limited for prognostic indication, predictive models, or patient management, and as a complement to biomarkers.